让机器人像人一样选稳当工具并规划抗干扰操作
Robustness-Aware Tool Selection and Manipulation Planning with Learned Energy-Informed Guidance
- 用能量模型衡量操作鲁棒性，指导工具选择与路径规划
- 在仿真和真实场景中均选出更稳的工具并生成抗扰轨迹
- 适合需要稳定抓取与操作的工业机器人任务
人类在使用工具时会无意识地选择更稳健的方式，例如用汤勺而非平铲舀肉丸。然而，机器人工具使用规划中对外部扰动的鲁棒性仍研究不足。本文提出一种鲁棒性感知方法，联合优化工具选择与高接触率操作轨迹，显式针对扰动优化鲁棒性。核心是基于能量的鲁棒性度量，引导规划器生成鲁棒操作行为。我们构建分层优化流程：先识别最优工具及配置以最大化鲁棒性，再规划维持鲁棒性的操作轨迹。在三个典型工具使用任务上评估，仿真与真实实验均表明，该方法能持续选择更鲁棒的工具，并生成抗扰能力强的操作计划。
原文摘要 · Abstract (English)
Humans subconsciously choose robust ways of selecting and using tools, for example, choosing a ladle over a flat spatula to serve meatballs. However, robustness under external disturbances remains underexplored in robotic tool-use planning. This paper presents a robustness-aware method that jointly selects tools and plans contact-rich manipulation trajectories, explicitly optimizing for robustness against disturbances. At the core of our method is an energy-based robustness metric that guides the planner toward robust manipulation behaviors. We formulate a hierarchical optimization pipeline that first identifies a tool and configuration that optimizes robustness, and then plans a corresponding manipulation trajectory that maintains robustness throughout execution. We evaluate our method across three representative tool-use tasks. Simulation and real-world results demonstrate that our method consistently selects robust tools and generates disturbance-resilient manipulation plans.
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